CN111339619A - LID system design method based on improved PSO algorithm - Google Patents
LID system design method based on improved PSO algorithm Download PDFInfo
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Abstract
An LID system design method based on an improved PSO algorithm comprises the following steps of ① site selection analysis, namely analyzing urban underlying surfaces from the current situation and the planned land angle through an integrated 3S technology, identifying the land range and the area upper limit of a cutoff well and a regulation and storage pool, ② carrying out secondary development on an SWMM model by adopting the improved PSO algorithm on the basis of site selection analysis results to complete LID facility arrangement and scale calculation.
Description
Technical Field
The invention belongs to a city runoff pollution control method, and particularly relates to a LID system design method based on an improved PSO algorithm.
Background
Urban runoff pollution control is one of the core targets of sponge city and black and odorous water treatment, and is a key problem to be solved for protecting water environment and realizing sustainable development and green development of social environment. The LID system scheme design is used as a source control means, and is one of core works for compiling urban runoff pollution control schemes. At present, a scientific and effective global systematic optimization design technology is lacked in the field, various types of LID facility construction scales are artificially and subjectively distributed mainly according to the runoff pollution source control rate index requirements, and the global optimal system scheme design is difficult to realize. Therefore, in actual design and engineering practice, the aims of saving environment, protecting the environment and building the environment with low carbon are difficult to further realize.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a LID system design method based on an improved PSO algorithm, which carries out secondary development on an SWMM drainage model by integrating a 3S technology and utilizing the improved PSO algorithm, and compared with a manual distribution accounting method, the method can determine the control index of the urban runoff pollution source with the minimum engineering investment.
As conceived above, the technical scheme of the invention is as follows: a LID system design method based on an improved PSO algorithm is characterized in that: the method comprises the following steps:
①, location analysis, namely, by integrating 3S technology, the city underlying surface is analyzed from the aspects of current situation and planned land, and the land scope and the area upper limit of the intercepting well and the regulating and storing pool can be identified, the concrete steps are as follows:
a. determining a design area boundary range;
b. analyzing and processing the original image data of the RS of the urban underlying surface, and identifying and classifying the underlying surface;
c. b, correcting the identification result of the underlying surface in the step b by using GPS on-site positioning;
d. c, processing and converting the result of the step c by using a GIS platform, and establishing a database of the current land based on the GIS platform;
e. establishing a planning land database based on a GIS platform according to a planning map;
f. establishing an artificial decision site selection database based on a GIS platform according to the site selection opinions of decision-makers;
g. performing spatial analysis on the databases established in the steps d, e and f by using the superposition analysis function of the GIS, and determining the addressable position and the upper limit of the used area of each LID facility;
② on the basis of the result of site selection analysis, an improved PSO algorithm is adopted to carry out secondary development on the SWMM model to complete LID facility arrangement and scale calculation, and the specific steps comprise:
a. partitioning the design area according to a partitioning calculation strategy;
b. initializing a particle swarm scheme in a partitioning mode according to a particle grouping initialization strategy;
c. calling an SWMM calculation engine to calculate the hydraulic water quality;
d. calculating the total construction cost and the runoff pollution source control rate;
e. comparing the land area with the available land area;
f. updating the positions of the particles which do not meet the runoff pollution source control rate or available land area constraint into the positions after the last iteration;
g. performing iterative updating on the particle swarm according to a grouping evolution strategy;
h. returning to the step 3 for iterative computation or meeting the step 7 for termination condition to terminate iterative computation;
i. and terminating the iterative calculation when all the available land areas are utilized or a specified iteration number is reached or the construction cost marginal decrement rate is smaller than a specified value.
The LID addressable location is selected according to the following table:
| LID facility type | LID facility settable position |
| Sunken green land | Bare soil of green land |
| Biological retention belt | Bare soil of green land |
| Rainwater garden | Bare soil of green land |
| Grass planting ditches, infiltration ditches, etc | Bare soil of green land |
| Permeable pavement | Residential road |
| Permeable pavement | Town road |
| Green roof | Building roof |
| Rain barrel | Lower part of the rain drop pipe |
Wherein the position of the rain drop pipe is determined by field location.
The particle grouping initialization strategy in the step b in the step ② is to initialize particles according to three dimensions of construction scale, pollutant control net amount and construction cost, so as to improve the dispersion and uniformity, specifically:
sorting LID facilities according to unit construction scale from high to low according to the control rate of the pollutant source of unit construction cost until all available land areas are completely utilized, and randomly generating an initial scheme by taking the basic scheme of construction cost, runoff pollution source control rate and construction cost as a base point;
II, sequencing LID facilities from high to low according to the control rate of the pollutant source of the unit land scale to the unit construction scale until all available land areas are completely utilized, and randomly generating an initial scheme by taking the basic scheme of the land scale, the pollutant control rate and the construction cost as a base point;
and III, sequencing LID facilities according to the unit construction scale from high to low according to the construction cost of the unit land scale until all available land areas are completely utilized, and randomly generating an initial scheme by taking the construction cost, the land scale and the pollutant control rate as base points.
The grouping evolution strategy of step g in the above step ② is:
each iteration firstly extracts the intra-group optimal value and the inter-group optimal value according to the initialized particle swarm scheme generated in the step b of ②, groups the intra-group optimization, exchanges optimization information among groups, specifically improves by increasing an intra-group speed influence operator through a speed updating formula,
vid＝w*vid+c1r1(pid-xid)+c2r2(pld-xid)+c3r3(pgd-xid) (1)
in the formula:
w is the inertia factor;
vid-the fundamental flying speed of the particles;
xid-the current position of the particle;
pid-an individual optimal position;
pld-an optimal position within the group;
pgd-a global optimal position;
c1r1-self-learning factor and randomness;
c2r2-intra-group learning factors and randomness;
c3r3-inter-group learning factor and randomness;
the invention has the following advantages and positive effects:
1. the invention provides an LID addressing analysis method by utilizing a 3S technology, which comprises the following steps: the method comprises the steps of establishing a current underlying surface identification database by utilizing RS influence and GPS positioning correction, establishing a planning land identification database by utilizing a land planning map, and analyzing the layout land and the upper limit of the area of the LID facility by utilizing the space analysis function of the GIS.
2. The method is based on the site selection analysis result, and adopts the improved PSO algorithm to carry out secondary development on the SWMM model, so as to complete LID facility arrangement and scale calculation, thereby achieving the aim of realizing the runoff pollution source control index with the minimum construction cost.
Drawings
FIG. 1 is a flow chart of site selection analysis;
fig. 2 is a flow chart for performing secondary development on the SWMM model by using an improved PSO algorithm based on the result of site selection analysis to complete LID facility arrangement and scale calculation.
Detailed Description
As shown in fig. 1 and 2: an LID system design method based on an improved PSO algorithm comprises the following steps:
1. site selection analysis, namely, analyzing the urban underlying surface from the current situation and planned land angle by integrating a 3S technology, identifying the land range and the area upper limit of a cutoff well and a regulation and storage pool, and specifically comprising the following steps:
① determining the design area boundary range;
② analyzing and processing the RS original image data of the urban underlying surface, and identifying and classifying the underlying surface;
③, correcting the identification result of the underlying surface in the step 2 by GPS on-site positioning;
④, processing and converting the result of step ③ by using a GIS platform, and establishing a database of the current land based on the GIS platform;
⑤, establishing a GIS platform-based land database for planning according to a planning map;
⑥, establishing an artificial decision site selection database based on a GIS platform according to the site selection opinions of decision-makers;
⑦, performing space analysis on the database established in steps ④, ⑤ and f ⑥ by using the superposition analysis function of the GIS, and determining addressable positions of each LID facility and the upper limit of the used area, wherein the addressable positions of the LID are selected according to the following table:
wherein the position of the rain drop pipe is determined by field positioning;
2. and (3) carrying out secondary development on the SWMM model by adopting an improved PSO algorithm on the basis of the site selection analysis result to complete LID facility arrangement and scale calculation, wherein the algorithm aims at the total construction cost of the LID facility and can be expressed as a function with the construction scale as an independent variable. The method has two constraint conditions, wherein firstly, the control rate of the runoff pollution source of the design scheme is greater than the specified control rate of the runoff pollution source unless all the constructable areas are fully utilized; and secondly, different LID facilities are arranged in corresponding allowable construction areas, and the area of the different LID facilities does not exceed the upper limit of the area of the allowable construction land. The method comprises the following specific steps:
①, partitioning the design area according to a partition calculation strategy, namely dividing the design area into a plurality of catchment partitions through DEM terrain in combination with space analysis functions of depression analysis, slope analysis and the like of a GIS, grouping particle swarms according to catchment partitions, and performing parallel calculation among groups.
②, initializing the particle swarm scheme in a partitioning manner according to the particle grouping initialization strategy;
the particle grouping initialization strategy initializes particles according to three dimensions of construction scale, pollutant control net amount and construction cost, and improves the dispersion and uniformity, and specifically comprises the following steps:
a. sequencing LID facilities according to the unit construction scale from high to low according to the pollutant source control rate of the unit construction cost until all available land areas are completely utilized, and randomly generating an initial scheme by taking the basic scheme of the construction cost, the runoff pollution source control rate and the construction cost as a base point;
b. sequencing LID facilities from high to low according to the control rate of the pollutant source of the unit land utilization scale to the unit construction scale until all available land areas are completely utilized, and randomly generating an initial scheme by taking the basic scheme of the land utilization scale, the pollutant control rate and the construction cost as a base point;
c. sequencing LID facilities according to the unit construction scale from high to low according to the construction cost of the unit land scale until all available land areas are fully utilized, and randomly generating an initial scheme by taking the construction cost, the land scale and the pollutant control rate as base points;
③ calling an SWMM calculation engine to calculate the hydraulic water quality;
④ calculating the total construction cost and the runoff pollution source control rate;
⑤ comparing the land area with the available land area;
⑥ updating the positions of the particles which do not satisfy the runoff pollution source control rate or the available land area constraint to the positions after the previous iteration;
⑦, performing iterative update on the particle swarm according to a grouping evolution strategy;
grouping evolution strategy
In each iteration, an intra-group optimal value and an inter-group optimal value are extracted according to an initial scheme generated by three dimensions in ②, intra-group optimization is grouped, optimization information is interacted among groups, and specifically, the intra-group speed influence operator is increased through a speed updating formula to improve.
vid＝w*vid+c1r1(pid-xid)+c2r2(pld-xid)+c3r3(pgd-xid) (1)
In the formula:
w is the inertia factor;
vid-the fundamental flying speed of the particles;
xid-the current position of the particle;
pid-an individual optimal position;
pld-an optimal position within the group;
pgd-a global optimal position;
c1r1-self-learning factor and randomness;
c2r2-intra-group learning factors and randomness;
c3r3-inter-group learning factor and randomness;
⑧ returns to step ③ to iterate or meet ⑦ to terminate the iterated;
⑨ terminating the iteration when all available land area is used or a specified number of iterations is reached or the construction cost margin decrement rate is less than a specified value.
Claims (4)
1. A LID system design method based on an improved PSO algorithm is characterized in that: the method comprises the following steps:
①, location analysis, namely, by integrating 3S technology, the city underlying surface is analyzed from the aspects of current situation and planned land, and the land scope and the area upper limit of the intercepting well and the regulating and storing pool can be identified, the concrete steps are as follows:
a. determining a design area boundary range;
b. analyzing and processing the original image data of the RS of the urban underlying surface, and identifying and classifying the underlying surface;
c. b, correcting the identification result of the underlying surface in the step b by using GPS on-site positioning;
d. c, processing and converting the result of the step c by using a GIS platform, and establishing a database of the current land based on the GIS platform;
e. establishing a planning land database based on a GIS platform according to a planning map;
f. establishing an artificial decision site selection database based on a GIS platform according to the site selection opinions of decision-makers;
g. performing spatial analysis on the databases established in the steps d, e and f by using the superposition analysis function of the GIS, and determining the addressable position and the upper limit of the used area of each LID facility;
② on the basis of the result of site selection analysis, an improved PSO algorithm is adopted to carry out secondary development on the SWMM model to complete LID facility arrangement and scale calculation, and the specific steps comprise:
a. partitioning the design area according to a partitioning calculation strategy;
b. initializing a particle swarm scheme in a partitioning mode according to a particle grouping initialization strategy;
c. calling an SWMM calculation engine to calculate the hydraulic water quality;
d. calculating the total construction cost and the runoff pollution source control rate;
e. comparing the land area with the available land area;
f. updating the positions of the particles which do not meet the runoff pollution source control rate or available land area constraint into the positions after the last iteration;
g. performing iterative updating on the particle swarm according to a grouping evolution strategy;
h. returning to the step 3 for iterative computation or meeting the step 7 for termination condition to terminate iterative computation;
i. and terminating the iterative calculation when all the available land areas are utilized or a specified iteration number is reached or the construction cost marginal decrement rate is smaller than a specified value.
2. The LID system design method based on the improved PSO algorithm as claimed in claim 1, wherein: the LID addressable location is selected according to the following table:
Wherein the position of the rain drop pipe is determined by field location.
3. The LID system design method based on improved PSO algorithm as claimed in claim 1, wherein the particle grouping initialization strategy of step b in step ② is to initialize particles according to three dimensions of construction scale, net amount of pollutant control and construction cost, to improve distribution and uniformity, specifically:
sorting LID facilities according to unit construction scale from high to low according to the control rate of the pollutant source of unit construction cost until all available land areas are completely utilized, and randomly generating an initial scheme by taking the basic scheme of construction cost, runoff pollution source control rate and construction cost as a base point;
II, sequencing LID facilities from high to low according to the control rate of the pollutant source of the unit land scale to the unit construction scale until all available land areas are completely utilized, and randomly generating an initial scheme by taking the basic scheme of the land scale, the pollutant control rate and the construction cost as a base point;
and III, sequencing LID facilities according to the unit construction scale from high to low according to the construction cost of the unit land scale until all available land areas are completely utilized, and randomly generating an initial scheme by taking the construction cost, the land scale and the pollutant control rate as base points.
4. The LID system design method based on improved PSO algorithm as claimed in claim 1, wherein the group evolution strategy of step g in the step ② is:
each iteration firstly extracts the intra-group optimal value and the inter-group optimal value according to the initialized particle swarm scheme generated in the step b of ②, groups the intra-group optimization, exchanges optimization information among groups, specifically improves by increasing an intra-group speed influence operator through a speed updating formula,
vid＝w*vid+c1r1(pid-xid)+c2r2(pld-xid)+c3r3(pgd-xid) (1)
in the formula:
w is the inertia factor;
vid-the fundamental flying speed of the particles;
xid-the current position of the particle;
pid-an individual optimal position;
pld-an optimal position within the group;
pgd-a global optimal position;
c1r1-self-learning factor and randomness;
c2r2-intra-group learning factors and randomness;
c3r3-inter-group learning factor and randomness.
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| CN113190945A (en) * | 2021-05-13 | 2021-07-30 | 西安理工大学 | Urban drainage pipe network optimization method based on online agent model assisted evolution algorithm |
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| CN113190945B (en) * | 2021-05-13 | 2022-04-12 | 西安理工大学 | Urban drainage pipe network optimization method based on online agent model assisted evolution algorithm |
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